Unsupervised representation learning, self-supervised learning (SSL), and contrastive learning are a family of training frameworks which have been successfully applied to addressing the small data problem. Unsupervised representation learning enables a network to learn strong visual representations without any supervision. Various forms include deep clustering [1]}, [2]}, [3]} or contrastive learning. These methods can be categorized as generative or discriminative and have been successful in the 2D domain. In this work, we focus on discriminative methods for 3D mesh data.
